YX-IDCRC: a general approach for calculating interdiffusion coefficients from raw experimental data and its application
Bibliographic record
Abstract
The Boltzmann-Matano method and its derivatives are widely used approaches for calculating interdiffusion coefficients. However, raw practical C-x curves typically have to be modified to fit with various functions due to their fluctuations, failing to reflect their essential characteristics. Herein, we introduce YX-IDCRC and YX-IDCRC software to calculate the interdiffusion coefficient using raw C-x curves. Firstly, we apply YX-IDCRC for the ideal (not fluctuate) and simulated (with noises) data, the calculated average of the ideal data closely matches the reported result. Secondly, we propose a method to determine the FWHM interval and distinguish the abnormal points by examining the calculated. Thirdly, we prepare the Cr2O3-CaO diffusion couples in the Fe2O3 matrix, and the average at 1273, 1298, 1323, and 1348 K are calculated as 3.10×10−9, 4.82×10−9, 7.02×10−9, and 2.4×10−8 cm2·s−1, respectively. Deduction from the Arrhenius equation indicates the diffusion activation energy (ED) as 369.97±51.80 kJ·mol−1, which lies in the reported results and verifies the reliability of YX-IDCRC on the practical diffusion systems. Finally, the supervision of limitation and applicability indicates that YX-IDCRC is applicable for the binary or pseudo-binary systems with S- or half-S-shape C-x curves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".